Hierarchical spatio-temporal autoregressive models are useful to understand
the impact of predictors on a spatio-temporal-dependent variable. This study
aims to fit the model to monthly PM10 concentration using potential predictors
from 33 monitoring stations within Peninsular Malaysia from 2006 to 2015 and
predict the space–time data spatially and temporally.

Categories: Kualiti, Partikel Terampai, Udara
Tags: Artikel Jurnal, Data Penerbitan, Sulit